Virtual reductant quality sensor

ABSTRACT

An exhaust treatment system is provided. The exhaust treatment system may include a selective catalyst reduction (SCR) unit for removing NO X  from exhaust; a reducing agent dispensing unit for providing a reducing agent to the SCR unit; a first NO X  sensor for indicating a NO X  emission level of the exhaust prior to the SCR unit; a second NO X  sensor for indicating a NO X  emission level of the exhaust after the SCR unit, wherein at least one of the first NO X  sensor and the second NO X  sensor is a virtual NO X  sensor; and a controller configured to electronically communicate with a virtual sensor network comprising a first virtual NO X  sensor and a virtual reducing agent quality sensor indicating a quality index of the reducing agent according to the first NO X  sensor and the second NO X  sensor.

TECHNICAL FIELD

Embodiments of the present disclosure pertain to a virtual sensor, and more particularly to a virtual sensor for a selective catalytic reduction system.

BACKGROUND

Growing government standards associated with combustion engine emissions have increased the burden on manufacturers to reduce the amount of nitrogen oxides (NO_(X)) and particulates that may be exhausted from their developed engines. Along with this burden is the manufacturer's commitment to their customers to produce fuel efficient engines.

One known type of NO_(X) reduction technique is selective catalytic reduction (SCR). This technique of reducing NO_(X) in a combustion engine generally includes the use of reductants, such as ammonia, aqueous urea, and other compounds in conjunction with an appropriate catalyst material.

In a conventional open loop control urea based SCR system, a urea pump may inject a urea solution into the exhaust stream of a combustion engine through an atomizer. An SCR controller may control the rate of urea that is being applied to the atomizer. Within the exhaust stream, the urea solution may decompose into ammonia (NH₃) and water vapor above certain temperatures, such as 160 degrees C. When the exhaust gas mixture is passed over a SCR catalyst, the NO_(X) and NH₃ molecules react with the catalyst and generally produce diatomic nitrogen (N₂), water (H₂O), and carbon dioxide (CO₂).

The performance of a SCR catalyst to reduce NO_(X) depends upon many factors, such as catalyst formulation, the size of the catalyst, exhaust gas temperature, and urea dosing rate. With regard to the dosing rate, the NO_(X) reduction efficiency tends to increase linearly until the dosing rate reaches a certain limit. Above the limit, the efficiency of the NO_(X) reduction may start to increase in a slower rate. One reason for the decline in the NO_(X) reduction efficiency is that the ammonia may be supplied at a faster rate that the NO_(X) reduction process can consume. The excess ammonia, known as ammonia slip, may be expelled from the SCR catalyst.

In order for an ideal reaction to take place, the integrity of the reductant (e.g., urea) must be maintained. For instance, if the reductant is diluted (e.g., in water) or overly concentrated, an ideal reaction in the SCR system will not occur. Thus, to promote an optimal reaction, it is beneficial to ensure the quality of the reductant.

Physical sensors are widely used in many products, to measure and monitor physical phenomena, such as temperature, speed, and emissions from motor vehicles. Physical sensors often take direct measurements of the physical phenomena and convert these measurements into measurement data to be further processed by control systems. Although physical sensors take direct measurements of the physical phenomena, physical sensors and their associated hardware are often costly and, sometimes, unreliable. For instance, directly measuring the quality of a reductant, such as urea, with physical sensors in a field environment is difficult and may be unreliable.

Instead of direct measurements, virtual sensors have been developed to process other various physically measured values and to produce values that were previously measured directly by physical sensors. For example, U.S. Pat. No. 5,386,373 (the '373 patent) issued to Keeler et al. on Jan. 31, 1995, discloses a virtual continuous emission monitoring system with sensor validation. The '373 patent uses a back propagation-to-activation model and a Monte Carlo search technique to establish and optimize a computational model used for the virtual sensing system to derive sensing parameters from other measured parameters.

A machine may utilize multiple sensors to function properly, and multiple virtual sensors may be used. However, conventional multiple virtual sensors are often used independently without taking into account other virtual sensors in an operating environment.

SUMMARY

According to aspects disclosed herein, a system and method are provided to detect the quality of a reductant according to a virtual sensor.

According to an aspect of an embodiment herein, an exhaust treatment system is disclosed. The exhaust treatment system including: a selective catalyst reduction (SCR) unit for removing NO_(X) from exhaust produced by an engine; a reducing agent dispensing unit for providing a reducing agent to the SCR unit; a first NO_(X) sensor for indicating a NO_(X) emission level of the exhaust prior to the SCR unit; a second NO_(X) sensor for indicating a NO_(X) emission level of the exhaust after the SCR unit, wherein at least one of the first NO_(X) sensor and the second NO_(X) sensor is a virtual NO_(X) sensor; and a controller configured to electronically communicate with a virtual sensor network comprising a first virtual NO_(X) sensor and a virtual reducing agent quality sensor, the virtual reducing agent quality sensor for indicating a quality index of the reducing agent, and wherein the quality index of the reducing agent is determined according to the first NO_(X) sensor and the second NO_(X) sensor.

According to another aspect of an embodiment disclosed herein, a method is provided to detect the quality of a reductant according to a virtual sensor. The method includes: configuring a virtual sensor network including at least a first virtual NO_(X) sensor for indicating a first NO_(X) emission level and a virtual reductant quality sensor for indicating a reductant quality index for a reductant received by a selective catalyst reduction (SCR) unit; obtaining a first NO_(X) value according to a first NO_(X) sensor indicating a NO_(X) emission level for engine exhaust prior to treatment by the SCR unit; obtaining a second NO_(X) value according to a second NO_(X) sensor indicating a NO_(X) emission level for engine exhaust after treatment by the SCR unit; and computing the reductant quality index according to the first NO_(X) value and the second NO_(X) value, wherein at least one of the first NO_(X) sensor and the second NO_(X) sensor is the first virtual NO_(X) sensor.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary machine according to a embodiment described herein;

FIG. 2 is a block diagram of a reductant quality detection system in an after-treatment system according to an embodiment herein;

FIG. 3 is a block diagram of a method of detecting reductant quality according to an embodiment herein.

DETAILED DESCRIPTION

Exemplary embodiments of the present invention are presented herein with reference to the accompanying drawings. Herein, like numerals designate like parts throughout.

FIG. 1 illustrates an exemplary machine 100 according to a embodiment described herein. The machine 100 may refer to any type of stationary or mobile machine that performs some type of operation associated with a particular industry. The machine 100 may also include any type of commercial vehicle, such as cars, trucks, vans, boats, ships, and other vehicles or machines, such as power generators and stationary gas compressors.

A machine 100 may include an engine 102, a selective catalytic reduction (SCR) unit 108, a reductant unit 106 (e.g., a urea reservoir/tank), sensors 120, and a controller 110. The engine 102 transmits exhaust to the SCR unit 108. The SCR unit 108 receives the exhaust from the engine 102, and receives a reductant from the reductant unit 106. The SCR unit 108 is configured to reduce the NO_(X) emission of the engine exhaust by using SCR.

The controller 110 is configured to send or receive information to the sensors 120. For instance the controller 110 may receive information from physical sensors (e.g., exhaust and/or reductant flow rate sensors, NO_(X) sensors, engine sensors, ambient condition sensors, etc.), or may generate or utilize preconfigured virtual sensors (e.g., a virtual NO_(X) sensor, a virtual urea sensor, etc.) at various points in the system.

The controller 110 maybe a processing system that monitors and controls operations of the machine 100. Controller 110 may be configured to collect information from various sensors 120 operating within the machine 100 to provide control signals that affect the operations of devices within the machine 100. In one embodiment of the present invention, the controller 110 may be part of an engine control module (ECM) that monitors and controls the operation of an engine 102 associated with machine 100. For example, the controller 110 may be a module programmed or hardwired within an ECM that performs functions dedicated to certain embodiments described herein. For example, the controller 110 may be implemented in software that is stored as instructions and data within a memory device of an ECM and is executed by a processor operating within the ECM. Alternatively, the controller 110 may be a module that is separate from other components of the system, and may be in electronic communication with other components of the system.

Controller 110 may include a processor, memory, and an interface. The processor may be a processing device, such as a microcontroller, that may exchange data with the memory and interface to perform certain processes consistent with features described herein. One skilled in the art would recognize that the controller 110 may include a plurality of processors that may operate collectively to perform functions consistent with certain embodiments presented herein.

The sensors 120 may include a combination of one or more physical and/or virtual sensors. For example, the sensors 120 may include one or more physical sensors provided for measuring certain parameters of machine operating environment, such as physical emission sensors for measuring emissions of machine 100, such as Nitrogen Oxides (NO_(X)), Sulfur Dioxide (SO₂), Carbon Monoxide (CO), total reduced Sulfur (TRS), etc. Physical sensors may include any appropriate sensors that are used with engine 102 or other machine components to provide various measured parameters about engine 102 or other components, such as temperature, speed, acceleration rate, fuel pressure, power output, etc.

A virtual sensor network, as used herein, may refer to a collection of virtual sensors integrated and working together using certain control algorithms such that the collection of virtual sensors may provide more desired or more reliable sensor output parameters than discrete individual virtual sensors. A virtual sensor network system may include a plurality of virtual sensors configured or established according to certain criteria based on a particular application. Additional sensors may provide information about the ambient environmental conditions, such as humidity, air temperature, and elevation.

A virtual sensor, as used herein, may refer to a mathematical algorithm or model that produces output measures comparable to a physical sensor based on inputs from other systems. For example, a physical NO_(X) emission sensor may measure the NO_(X) emission level of machine 100 and provide values of NO_(X) emission level to other components, such a controller 110; while a virtual NO_(X) emission sensor may provide calculated values of NO_(X) emission level to a controller 110 based on other measured or calculated parameters, such as such as compression ratios, turbocharger efficiency, after cooler characteristics, temperature values, pressure values, ambient conditions, fuel rates, and engine speeds, etc. The term “virtual sensor” may be used interchangeably with “virtual sensor model.”

The virtual sensor network system may also facilitate or control operations of the virtual sensors. The virtual sensors may include any appropriate virtual sensor providing sensor output parameters corresponding to one or more physical sensors in machine 100.

Further, virtual sensor network system may be configured as a separate control system or, alternatively, may coincide with other control systems such as an ECM. Virtual sensor network system may also operate in series with or in parallel to an ECM. Virtual sensor network system and/or ECM may be implemented by any appropriate computer system. Thus, the virtual sensor network system may be implement on the controller 110, or e.g., may be implement elsewhere and communications therewith may be relayed through the controller 110. Additionally, a computer system may also be configured to design, train, and validate virtual sensors in virtual sensor network and other component of machine 100.

A virtual sensor process model may be established to build interrelationships between physical and virtual sensors. After virtual sensor process model is established, values of input parameters may be provided to virtual sensor process model (e.g., the controller 110) to generate values of output parameters based on the given values of input parameters and the interrelationships between input parameters and output parameters established by the virtual sensor process model.

In certain embodiments, virtual sensor system may include a NO_(X) virtual sensor to provide levels of NO_(X) emitted from an exhaust system of machine 100, and a virtual reductant sensor to provide a quality level (or quality index) of the reductant stored in the reductant unit 106 and transmitted to the SCR unit 108. Input parameters may include any appropriate type of data associated with NO_(X) emission levels. For example, input parameters may include parameters that control operations of various response characteristics of engine 110 and/or parameters that are associated with conditions corresponding to the operations of engine 110. For instance, input parameters may include fuel injection timing, compression ratios, turbocharger efficiency, after cooler characteristics, temperature values (e.g., intake manifold temperature), pressure values (e.g., intake manifold pressure), ambient conditions (e.g., ambient humidity), fuel rates, and engine speeds, etc. Other parameters, however, may also be included. For example, parameters originated from other vehicle systems, such as chosen transmission gear, axle ratio, elevation and/or inclination of the vehicle, etc., may also be included. Further, input parameters may be measured by certain physical sensors, or created by other control systems such as an ECM.

A virtual sensor process model may include any appropriate type of mathematical or physical model indicating interrelationships between input parameters and output parameters. For example, virtual sensor process model may be a neural network based mathematical model that is trained to capture interrelationships between input parameters and output parameters. Other types of mathematic models, such as fuzzy logic models, linear system models, and/or non-linear system models, etc., may also be used. Virtual sensor process model may be trained and validated using data records collected from a particular engine application for which virtual sensor process model is established. That is, virtual sensor process model may be established according to particular rules corresponding to a particular type of model using the data records, and the interrelationships of virtual sensor process model may be verified by using part of the data records.

After virtual sensor process model is trained and validated, virtual sensor process model may be optimized to define a desired input space of input parameters and/or a desired distribution of output parameters. The validated or optimized virtual sensor process model may be used to produce corresponding values of output parameters when provided with a set of values of input parameters.

Thus, a controller 110 may be configured to generate or to utilize a preconfigured virtual sensor model to determine predicted NO_(X) values based on a model reflecting a predetermined relationship between control parameters and NO_(X) emissions, wherein the control parameters include ambient humidity, manifold pressure, manifold temperature, fuel rate, and engine speed associated with the engine. Additional sensors may provide information about the ambient environmental conditions, such as humidity, air temperature, and elevation. Additionally, the virtual sensor network can utilize additional sensors for detecting the flow rate of the exhaust through the SCR and the flow rate of the reductant through the SCR.

If the controller 110 (or the ECM or processor operating the virtual network) determines that any individual input parameter or output parameter is out of the respective range of the input space or output space, the controller may send out a notification to other computer programs, control systems, or a user of machine 100.

Optionally, controller 110 (or the ECM or processor operating the virtual network) may also apply any appropriate algorithm to maintain the values of input parameters or output parameters in the valid range to maintain operation with a reduced capacity. For instance, reducing the engine speed to reduce the flow rate of the exhaust, or increase the flow rate of the reductant in order to increase the reduction of NO_(X).

The controller 110 (or the ECM or processor operating the virtual network) may also determine collectively whether the values of input parameters are within a valid range. For example, a processor may use a Mahalanobis distance to determine normal operational condition of collections of input values.

During training and optimizing of virtual sensor models, a valid Mahalanobis distance range for the input space may be calculated and stored as calibration data associated with individual virtual sensor models. In operation, a processor may calculate a Mahalanobis distance for input parameters of a particular virtual sensor model as a validity metric of the valid range of the particular virtual sensor model. If the calculated Mahalanobis distance exceeds the range of the valid Mahalanobis distance range stored in the virtual sensor network, the controller 110 may send out a notification to other computer programs, control systems, or a user of machine 100 to indicate that the particular virtual sensor may be unfit to provide predicted values.

Other validity metrics may also be used. For example, a processor may evaluate each input parameter against an established upper and lower bounds of acceptable input parameter values and may perform a logical AND operation on a collection of evaluated input parameters to obtain an overall validity metric of the virtual sensor model.

After monitoring and controlling individual virtual sensors, the controller 110 (e.g., virtual sensor network processor) may also monitor and control collectively a plurality of virtual sensor models. That is, the controller 110 may determine and control operational fitness of virtual sensor network. A processor may monitor any operational virtual sensor model. The processor may also determine whether there is any interdependency among any operational virtual sensor models including the virtual sensor models becoming operational. If the controller 110 determines there is an interdependency between any virtual sensor models, processor 202 may determine that the interdependency between the virtual sensors may have created a closed loop to connect two or more virtual sensor models together, which may be neither intended nor tested.

The controller 110 may then determine that virtual sensor network may be unfit to make predictions, and may send a notification or report to control systems, such as ECM, or users of the machine 100. That is, the controller (or e.g., a processor) may present other control systems or users the undesired condition via a sensor out-put interface. Alternatively, the controller may indicate as unfit only the interdependent virtual sensors, while keeping the remaining virtual sensors in operation.

As used herein, a decision that a virtual sensor or a virtual sensor network is unfit is intended to include any instance in which any input parameter to or any output parameter from the virtual sensor or the virtual sensor network is beyond a valid range or is uncertain; or any operational condition that affects the predictability and/or stability of the virtual sensor or the virtual sensor network. An unfit virtual sensor network may continue to provide sensing data to other control systems using virtual sensors not affected by the unfit condition, such as interdependency, etc.

The controller 110 may also resolve unfit conditions resulting from unwanted interdependencies between active virtual sensor models by deactivating one or more models of lower priority than those remaining active virtual sensor models.

For instance, if a first active virtual sensor model has a high priority for operation of machine 100 but has an unresolved interdependency with a second active virtual sensor having a low priority for operation of machine 100, the second virtual sensor model may be deactivated to preserve the integrity of the first active virtual sensor model.

FIG. 2 is a block diagram of a reductant quality detection system in an after-treatment system according to an embodiment herein. According to an embodiment herein, an exhaust treatment system 200 includes an engine 102, a selective catalytic reduction (SCR) unit 108, a reductant unit 106 (e.g., a urea reservoir/tank), sensors 202-208, and a controller 110. Sensors 202-208 are electronically coupled to controller 110 and may be physical or virtual sensors.

The reductant unit 106 is for holding a reductant, such as urea, ammonia or any other reductant according to the specific SCR system. Additionally, an optional filter 104 (e.g., a diesel particulate filter (DPF)) may be included between the engine 102 and the SCR 108 for reducing the amount of particulates in exhaust.

The engine 102 may generate exhaust which is transmitted to the SCR 108 for reducing the amount of NO_(X) in the exhaust. By passing the exhaust through a DPF 104, prior to the SCR 108 particulates in the exhaust may be removed. Removing particulates from exhaust prior to use of a physical NO_(X) sensor may increase the operational life of the sensor.

According to an embodiment herein the reductant unit 106 is configured to supply a urea reductant, to the SCR 108 for reducing the exhaust NO_(X). For instance, the urea from the reductant unit 106 may be combined with the exhaust from the engine 102 upstream of the SCR 108 in order to mix with the exhaust prior to entering the SCR 108.

According to one embodiment, engine sensor 202 is a physical sensor which may be used by the controller to predict a NO_(X) emission value. The physical sensor may be a single sensor or may reflect a combination of sensors for detecting parameters such as ambient humidity, manifold pressure, manifold temperature, fuel rate, and engine speed associated with the engine. Additionally, a first NO_(X) sensor 204 may be a physical NO_(X) sensor located upstream of the SCR 108 or may be a virtual NO_(X) sensor generated by the controller 110 based on variables such as those provided by the engine sensor 202. A second NO_(X) sensor 206 may be a physical NO_(X) sensor located downstream of the SCR 108 or may be a virtual NO_(X) sensor generated by the controller 110 based on variables such as those provided by the engine sensor 202.

According to embodiments herein, the reductant quality sensor 208 is a virtual reductant quality sensor. According to embodiments herein, the reductant may be urea. Thus, according to embodiments herein the controller, may generate a reductant (e.g., urea) quality of the reductant in the reductant unit 106 according to the differences (or lack thereof) between the first NO_(X) sensor 204 and the second NO_(X) sensor 206. For instance, when the second NO_(X) sensor imputes a NO_(X) value for the exhaust downstream of the SCR equal to that (or greater than an anticipated value), the controller may indicate that the reductant quality is degraded (e.g., diluted). Additionally, such a difference may indicate that reductant (e.g., urea) is not being properly transmitted to the SCR 108. Additionally, the virtual network model may incorporate other physical or virtual sensors to generate or refine the reductant (e.g., urea) quality sensor 208. For instance, additional sensors for detecting the flow rate of the exhaust through the SCR and the flow rate of the reductant through the SCR may be used to determine urea quality.

The controller may register variables such as temperature or time-of-last-fill of the reductant unit 106 to help determine a cause of the deviation from the anticipated NO_(X) values. However, when a reduction in NO_(X) between the first and second NO_(X) sensors 204, 206 are detected, then a NO_(X) reduction is taking place in the SCR 108. The controller 110 may generate a reductant (e.g., urea) quality according to the difference in NO_(X) sensors 204, 206 and the virtual model for the reductant quality sensor 208.

One or both of the first and second NO_(X) sensors 204, 206 may be a virtual sensor. Additionally, the controller 110 may be further configured to generate a signal when the quality index of the reducing agent indicated by the virtual reducing agent quality sensor is not within a tolerance level (e.g., a predefined tolerance level). For example, the control may be configured to trigger a warning light or adjust the flow rate of the reductant. For instance, if the NO_(X) reduction is less than expected, the controller may generate a signal to increase the amount of reductant to send to the SCR.

FIG. 3 is a block diagram of a method of detecting reductant quality according to an embodiment herein. According to FIG. 3, a method for detecting a reducing agent quality 300 includes a configuring a virtual sensor network step 302, an obtaining a first NO_(X) value step 304, an obtaining a second NO_(X) value step, a computing the reductant quality step 308. Optionally, the method 300 may also include a generating an out-of-tolerance signal step 310.

Additionally, configuring a virtual sensor network step 302 includes using a virtual sensor network which includes at least a first virtual NO_(X) sensor for indicating a first NO_(X) emission level and a virtual reductant quality sensor for indicating a reductant quality index for a reductant received by a selective catalyst reduction (SCR) unit.

The obtaining a first NO_(X) value step 304 includes determining the first NO_(X) value according to a first NO_(X) sensor indicating a NO_(X) emission level for engine exhaust prior to treatment by the SCR unit. The obtaining a second NO_(X) value step 306 includes determining a value according to a second NO_(X) sensor indicating a NO_(X) emission level for engine exhaust after treatment by the SCR unit. Additionally, the computing the reductant quality step 308 includes generating a virtual urea quality sensor according to the first NO_(X) value and the second NO_(X) value. Additionally, according to embodiments herein, at least one of the first NO_(X) sensor and the second NO_(X) sensor is the first virtual NO_(X) sensor.

Additionally, the generating an out-of-tolerance signal step 310 may include further generating a signal when the quality index of the reducing agent indicated by the virtual reducing agent quality sensor is not within a tolerance level (e.g., a predefined tolerance level). The out-of-tolerance signal step 310 may further include, but is not limited to, triggering a warning light or adjusting the flow rate of the reductant. For instance, if the NO_(X) reduction is less than expected, the controller may generate a signal to increase the amount of reductant to send to the SCR.

INDUSTRIAL APPLICABILITY

The disclosed reductant quality sensing system may be implemented in an after-treatment exhaust system in various machines. A reductant quality sensing system provides for enhanced reliability of the NO_(X) reduction process by verifying the integrity of the reductant.

Although certain embodiments have been illustrated and described herein for purposes of description, it will be appreciated by those of ordinary skill in the art that a wide variety of alternate and/or equivalent embodiments or implementations calculated to achieve the same purposes may be substituted for the embodiments shown and described without departing from the scope of the present invention. Those with skill in the art will readily appreciate that embodiments in accordance with the present invention may be implemented in a very wide variety of ways. This application is intended to cover any adaptations or variations of the embodiments discussed herein. Therefore, it is intended that embodiments in accordance with the present invention be limited only by the claims and the equivalents thereof. 

1. An exhaust treatment system comprising: a selective catalyst reduction (SCR) unit for removing NO_(X) from exhaust produced by an engine; a reducing agent dispensing unit for providing a reducing agent to the SCR unit; a first NO_(X) sensor for indicating a NO_(X) emission level of the exhaust prior to the SCR unit; a second NO_(X) sensor for indicating a NO_(X) emission level of the exhaust after the SCR unit, wherein at least one of the first NO_(X) sensor and the second NO_(X) sensor is a virtual NO_(X) sensor; and a controller configured to electronically communicate with a virtual sensor network comprising a first virtual NO_(X) sensor and a virtual reducing agent quality sensor, the virtual reducing agent quality sensor for indicating a quality index of the reducing agent, and wherein the quality index of the reducing agent is determined according to the first NO_(X) sensor and the second NO_(X) sensor.
 2. The exhaust treatment system of claim 1, wherein the virtual sensor network further comprises an exhaust flow rate sensor for detecting a flow rate of the exhaust through the SCR unit, and a reductant flow rate sensor for detecting the flow rate of the reductant to the SCR unit.
 3. The exhaust treatment system of claim 1, wherein the reducing agent is urea.
 4. The exhaust treatment system of claim 1, further comprising an exhaust filter coupled between the engine and the SCR unit.
 5. The exhaust treatment system of claim 4, wherein the exhaust filter is a diesel particulate filter (DPF).
 6. The exhaust treatment system of claim 5, wherein the first NO_(X) sensor is for further indicating a NO_(X) emission level between the DPF and the SCR unit.
 7. The exhaust treatment system of claim 1, wherein the first NO_(X) sensor is the first virtual NO_(X) sensor.
 8. The exhaust treatment system of claim 1, wherein the second NO_(X) sensor is the first virtual NO_(X) sensor.
 9. The exhaust treatment system of claim 1, wherein the first NO_(X) sensor is the first virtual NO_(X) sensor and the second NO_(X) sensor is a second virtual NO_(X) sensor.
 10. The exhaust treatment system of claim 1, wherein the controller is further configured to generate a signal when the quality index of the reducing agent indicated by the virtual reducing agent quality sensor is not within a tolerance level.
 11. The exhaust treatment system of claim 10, wherein reducing agent dispensing unit is configured to increase or decrease a flow rate of the reductant according to the signal.
 12. A method for detecting a reducing agent quality comprising: configuring a virtual sensor network comprising at least a first virtual NO_(X) sensor for indicating a first NO_(X) emission level and a virtual reductant quality sensor for indicating a reductant quality index for a reductant received by a selective catalyst reduction (SCR) unit; obtaining a first NO_(X) value according to a first NO_(X) sensor indicating a NO_(X) emission level for engine exhaust prior to treatment by the SCR unit; obtaining a second NO_(X) value according to a second NO_(X) sensor indicating a NO_(X) emission level for engine exhaust after treatment by the SCR unit; and computing the reductant quality index according to the first NO_(X) value and the second NO_(X) value, wherein at least one of the first NO_(X) sensor and the second NO_(X) sensor is the first virtual NO_(X) sensor.
 13. The method of claim 12, wherein the reductant is urea.
 14. The method of claim 12, wherein the first NO_(X) sensor further indicates a NO_(X) emission level for the engine exhaust after treatment by an engine filter.
 15. The method of claim 14, wherein the engine filter is a diesel particulate filter (DPF).
 16. The method of claim 12, wherein the first NO_(X) sensor is the first virtual NO_(X) sensor.
 17. The method of claim 12, wherein the second NO_(X) sensor is the first virtual NO_(X) sensor.
 18. The method of claim 12, wherein the first NO_(X) sensor is the first virtual NO_(X) sensor and the second NO_(X) sensor is a second virtual NO_(X) sensor.
 19. The method of claim 12, further comprising generating a signal when the reductant quality index indicated by the virtual reductant quality sensor is not within a tolerance level.
 20. The method of claim 19, further comprising adjusting a flow rate of the reductant according to the signal. 